GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly...

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Bibliographic Details
Main Authors: Ji, C.-M (Author), Li, L. (Author), Ni, J.-C (Author), Su, Y.-S (Author), Wang, Y.-T (Author), Zheng, C.-H (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
algorithm
Algorithms
Area Under Curve
area under the curve
Article
autoencoder
biological model
biology
breast tumor
clinical effectiveness
Computational Biology
controlled study
cross validation
disease association
embedding
gene sequence
genetic association
genetic predisposition
Genetic Predisposition to Disease
genetics
graph convolutional autoencoder
human
Humans
intermethod comparison
lung tumor
measurement accuracy
metabolism
microRNA
MicroRNAs
Models, Genetic
neoplasm
Neoplasms
Neural Networks, Computer
prediction
procedures
receiver operating characteristic
sequence similarity
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